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Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling.

, , , and . ACL (1), page 896-905. Association for Computational Linguistics, (2017)

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Why ADAGRAD Fails for Online Topic Modeling., , and . EMNLP, page 446-451. Association for Computational Linguistics, (2017)Fast Inference for Interactive Models of Text., , , and . COLING, page 2997-3006. ACL, (2016)Movie Recommendations Using the Deep Learning Approach., and . IRI, page 47-54. IEEE, (2018)Cross-referencing Using Fine-grained Topic Modeling., , , , , and . NAACL-HLT (1), page 3978-3987. Association for Computational Linguistics, (2019)Is Your Anchor Going Up or Down? Fast and Accurate Supervised Topic Models., , , , and . HLT-NAACL, page 746-755. The Association for Computational Linguistics, (2015)Labeled Anchors and a Scalable, Transparent, and Interactive Classifier., , , , and . EMNLP, page 824-829. Association for Computational Linguistics, (2018)Automatic Evaluation of Local Topic Quality., , , , , , and . ACL (1), page 788-796. Association for Computational Linguistics, (2019)Mrs: High Performance MapReduce for Iterative and Asynchronous Algorithms in Python., , , and . PyHPC@SC, page 76-85. IEEE, (2016)Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling., , , and . ACL (1), page 896-905. Association for Computational Linguistics, (2017)Mrs: MapReduce for Scientific Computing in Python., , and . SC Companion, page 600-608. IEEE Computer Society, (2012)